Comment by ryeguy_24
21 hours ago
How many proprietary use cases truly need pre-training or even fine-tuning as opposed to RAG approach? And at what point does it make sense to pre-train/fine tune? Curious.
21 hours ago
How many proprietary use cases truly need pre-training or even fine-tuning as opposed to RAG approach? And at what point does it make sense to pre-train/fine tune? Curious.
I'm thinking stuff like this:
https://denverite.com/2026/03/12/ai-recycling-facility-comme...
You could take a model like the one referenced in the article, retool it with Forge for oh I don't know, compost, and use it to flag batches that contain too much paper for instance.
These kinds of applications would work across industries, basically anywhere where you have a documented process and can stand to have automated oversight.
You can fine tune small, very fast and cheap to run specialized models ie. to react to logs, tool use and domain knowledge, possibly removing network llm comms altogether etc.
rag basically gives the llm a bunch of documents to search thru for the answer. What it doesn't do is make the algorithm any better. pre-training and fine-tunning improve the llm abaility to reason about your task.
RAG is dead
Using tools and skills to retrieve data or files is anything but dead.
I think people just mean "using vector databases to enable RAG".
4 replies →
And yet your blog says you think NFTs are alive. Curious.
But seriously, RAG/retrieval is thriving. It'll be part of the mix alongside long context, reranking, and tool-based context assembly for the forseeable future.
The issue I had with RAG when I tried building our own internal chat/knowledge bot was pulling in the relevant knowledge before sending to the LLM. Domain questions like "What is Cat Block B?" are common and, for a human, provide all the context that is needed for someone to answer within our org. But vectorizing that and then finding matching knowledge produced so many false positives. I tried to circumvent that by adding custom weighting based on keywords, source (Confluence, Teams, Email), but it just seemed unreliable. This was probably a year ago and, admittedly, I was diving in head first without truly understanding RAG end to end.
Being able to just train a model on all of our domain knowledge would, I imagine, produce much better results.
I don't think RAG is dead, and I don't think NFTs have any use and think that they are completely dead.
But the OP's blog is more about ZK than about NFTs, and crypto is the only place funding work on ZK. It's kind of a devil's bargain, but I've taken crypto money to work on privacy preserving tech before and would again.
Not OP, but...
> Of course you would have to set a temperature of 0 to prevent abuse from the operator, and also assume that an operator has access to the pre-prompt
Doesn't the fact that LLM's are still non-deterministic with a 0 temperature render all of this moot? And why was I compelled to read a random blog post on the unsolved issue of validating natural language? It's a SQL injection except without a predetermined syntax to validate against, and thus a NP problem we've yet to solve.
I have no interest in anything crypto, but they are making a proposal about NFTs tied to AI (LLMs and verifiable machine learning) so they can make ownership decisions.
So it'd be alive in the making decisions sense, not in a "the technology is thriving" sense.
Wait, what does NFTs have to do with RAG?
2 replies →
Is it??
In what, X's hype circles? Embeddings are used in production constantly.